In order to work properly, service robots need to reason about human environments accurately. One of the contributions of this paper is the analysis of a large real-world indoor topological database. We have shown experimentally that the presented methods predict indoor topologies accurately. To the best of our knowledge, no previous work exists on analyzing and using a large real-world floorplan database for anticipating indoor topologies. Furthermore, we have shown that indoor topologies consists of functional smaller parts which in turn can be used to develop methods with better prediction results. The reason for this is such methods capture the rationale behind manmade indoor spaces.

Future work consists of modeling the number of room types, extending the database with data from other environments such as KTH campus, making use of the metric coordinates in the data to have richer predictions and investigate how the predictions generalizes for different locations.
 
